// Appendix3.5

log using Appendix3.5.log, replace

use "C:\Users\sbstjp\OneDrive - Cardiff University\UKPVS.dta" // Prosser, Magasin, Proulx and Haddock, UK Progressive Values Dataset, September 2024 // Accessed on March 20 2025

// Create a weight

gen weight = 1

* Code age into categories - the other variables are already in such categories
recode ageNew (min/24=1 "0-24") (25/34=2 "25-34") (35/44=3 "35-44") (45/54=4 "45-54") (55/max=5 "55+"), generate(age_group)

* Generate totals for the weights - these are based on BESW29 as this dataset has political selfid, unlike census data
replace gender=. if inrange(gender, 3, 5)
rename gender FemaleGender
gen sextot=.
replace sextot = 0.50 if FemaleGender == 1 // Male
replace sextot = 0.50 if FemaleGender == 2 // Female

gen agetot=.
replace agetot = 0.13 if age_group == 1 //18-24
replace agetot = 0.14 if age_group == 2 //25-34
replace agetot = 0.19 if age_group == 3 //35-44
replace agetot = 0.19 if age_group == 4 //45-54
replace agetot = 0.35 if age_group == 5 //55+

gen ethtot=.
replace ethtot = 0.86 if Ethnicity2 == 1 // White
replace ethtot = 0.06 if Ethnicity2 == 2 // Asian
replace ethtot = 0.04 if Ethnicity2 == 3 // Black
replace ethtot = 0.04 if Ethnicity2 == 4 // Mixed

gen edcats=.
replace edcats=1 if inrange(Education, 1, 15) // Unidiplomaandbelow
replace edcats=2 if Education==16 // Undergraddegree
replace edcats=3 if inrange(Education, 17, 18) // Postgradandabove

gen edtot=.
replace edtot = 0.4740 if edcats == 1 // Unidiplomaandbelow
replace edtot = 0.2969 if edcats == 2 // Undergraddegree
replace edtot = 0.2291 if edcats == 3 // Postgradandabove

gen inccats=.
replace inccats=1 if inrange(Household_Income, 1, 6) //under 30k
replace inccats=2 if inrange(Household_Income, 7, 11) //30-60k
replace inccats=3 if inlist(Household_Income, 12, 13) //60-100k
replace inccats=4 if inlist(Household_Income, 14, 15) //over 100k

gen inctot=.
replace inctot = 0.3806 if inccats == 1 //under 30k
replace inctot = 0.3524 if inccats == 2 //30-60k
replace inctot = 0.1922 if inccats == 3 //60-100k
replace inctot = 0.0747 if inccats == 4 //over 100k

* Rake the weights using the Stata survwgt package
survwgt rake weight , by(FemaleGender age_group Ethnicity2 edcats inccats) totvars(sextot agetot ethtot edtot inctot) generate(rakedweight)

// Demographics

*Delete missing values and recode
replace Household_Income=. if Household_Income==999

gen ReligiousAtt=.
replace ReligiousAtt=1 if ReligiousAttendance==7
replace ReligiousAtt=0 if inlist(ReligiousAttendance, 1, 2, 3, 4, 5, 6, 9)

egen age_centered = mean(ageNew) 
replace age_centered = ageNew - age_centered
gen age_centered_squared = age_centered^2

*Create dummies
gen Graduate=. 
replace Graduate=0 if inrange(Education, 1, 15)
replace Graduate=1 if inrange(Education, 16, 18)

gen Married=.
replace Married=1 if inlist(MaritalStatus, 1, 2)
replace Married=0 if inrange(MaritalStatus, 3, 8)

gen Unemployed=. 
replace Unemployed=0 if inrange(employment, 1, 3)
replace Unemployed=1 if employment==4

// Life Satisfaction
*Reverse so satisfaction is coded high
foreach var in LifeSatisfaction {
    qui sum `var'
    local max_value = r(max)
    gen r`var' = `max_value' + 1 - `var'
}

*Standardize from 1-2, so it's like other dependent variables in the book
foreach var in rLifeSatisfaction {
    summarize `var'
    gen s`var' = 1 + (`var' - r(min)) / (r(max) - r(min))
}

rename srLifeSatisfaction LifeSat

// Standardize variables 
egen Age = std(ageNew)
egen Income = std(Household_Income)
rename PVS pvs1
egen PVS = std(pvs1)

// Regressions
regress LifeSat PVS [pweight=rakedweight], robust 
eststo
regress LifeSat PVS Age FemaleGender Graduate Income Married ReligiousAtt [pweight=rakedweight], robust // one without unemployment, as it reduces the obs 
eststo
regress LifeSat PVS Age FemaleGender Graduate Income Married ReligiousAtt Unemployed [pweight=rakedweight], robust 
eststo
esttab

log close

eststo clear

esttab using "Appendix3.5.rtf", ///
    b(3) se(3) ///
    star(* 0.05 ** 0.01 *** 0.001) ///
    coeflabels(_cons "Constant") ///
    nonumbers replace




